{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 对连续型特征，可以用哪个函数可视化其分布？（给出你最常用的一个即可），并根据代码运行结果给出示例。（10分） \n",
    "直方图 distplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "df = pd.read_csv(\"boston_housing.csv\")\n",
    "fig = plt.figure()\n",
    "sns.distplot(df[\"MEDV\"], bins=30, kde=True)\n",
    "plt.xlabel(\"Median value of owner-occupied homes\", fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 对两个连续型特征，可以用哪个函数得到这两个特征之间的相关性？根据代码运行结果，给出示例。（10分） \n",
    "DataFrame的corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x5f35048>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = df.columns \n",
    "data_corr = df.corr()\n",
    "sns.heatmap(data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. 如果发现特征之间有较强的相关性，在选择线性回归模型时应该采取什么措施。（10分） \n",
    "如果特征之间高度相关，可考虑进行PCA降维（特征层面）或加正则项（模型层面）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. 当采用带正则的模型以及采用随机梯度下降优化算法时，需要对输入（连续型）特征进行去量纲预处理。课程代码给出了用标准化（StandardScaler）的结果，请改成最小最大缩放（MinMaxScaler）去量纲 （10分），并重新训练最小二乘线性回归、岭回归、和Lasso模型（30分）。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RAD_1</th>\n",
       "      <th>RAD_2</th>\n",
       "      <th>RAD_3</th>\n",
       "      <th>RAD_4</th>\n",
       "      <th>RAD_5</th>\n",
       "      <th>RAD_6</th>\n",
       "      <th>RAD_7</th>\n",
       "      <th>RAD_8</th>\n",
       "      <th>RAD_24</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RAD_1  RAD_2  RAD_3  RAD_4  RAD_5  RAD_6  RAD_7  RAD_8  RAD_24\n",
       "0      1      0      0      0      0      0      0      0       0\n",
       "1      0      1      0      0      0      0      0      0       0\n",
       "2      0      1      0      0      0      0      0      0       0\n",
       "3      0      0      1      0      0      0      0      0       0\n",
       "4      0      0      1      0      0      0      0      0       0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从原始数据中分离输入特征x和输出y\n",
    "y = df['MEDV']\n",
    "X = df.drop('MEDV', axis = 1)\n",
    "# 尝试对y（房屋价格）做log变换，对log变换后的价格进行估计\n",
    "log_y = np.log1p(y)\n",
    "# RAD的含义是距离高速公路的便利指数。虽然给的数值是数值型，但实际是索引，可换成离散特征/类别型特征编码试试。\n",
    "X[\"RAD\"].astype(\"object\")\n",
    "X_cat = X[\"RAD\"]\n",
    "X_cat = pd.get_dummies(X_cat, prefix=\"RAD\")\n",
    "X = X.drop(\"RAD\", axis = 1)\n",
    "#特征名称，用于保存特征工程结果\n",
    "feat_names = X.columns\n",
    "X_cat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 最小最大缩放（MinMaxScaler）去量纲 （10分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据标准化\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "# 分别初始化对特征和目标值的最小最大缩放（MinMaxScaler）去量纲\n",
    "ss_X = MinMaxScaler()\n",
    "ss_y = MinMaxScaler()\n",
    "\n",
    "ss_log_y = MinMaxScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征以及目标值进行标准化处理\n",
    "# 对训练数据，先调用fit方法训练模型，得到模型参数；然后对训练数据和测试数据进行transform\n",
    "X = ss_X.fit_transform(X)\n",
    "\n",
    "#对y做标准化不是必须\n",
    "#对y标准化的好处是不同问题的w差异不太大，同时正则参数的范围也有限\n",
    "y = ss_y.fit_transform(y.values.reshape(-1, 1))\n",
    "log_y = ss_y.fit_transform(log_y.values.reshape(-1, 1))\n",
    "\n",
    "fe_data = pd.DataFrame(data = X, columns = feat_names, index = df.index)\n",
    "fe_data = pd.concat([fe_data, X_cat], axis = 1, ignore_index=False)\n",
    "\n",
    "#加上标签y\n",
    "fe_data[\"MEDV\"] = y\n",
    "fe_data[\"log_MEDV\"] = log_y\n",
    "\n",
    "#保存结果到文件\n",
    "fe_data.to_csv('FE_boston_housing.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 并重新训练最小二乘线性回归、岭回归、和Lasso模型（30分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 21)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"FE_boston_housing.csv\")\n",
    "# 从原始数据中分离输入特征x和输出y\n",
    "y = df[\"MEDV\"]\n",
    "\n",
    "X = df.drop([\"MEDV\", \"log_MEDV\"], axis = 1)\n",
    "\n",
    "#特征名称，用于后续显示权重系数对应的特征\n",
    "feat_names = X.columns\n",
    "#将数据分割训练数据与测试数据\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 随机采样20%的数据构建测试样本，其余作为训练样本\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33, test_size=0.2)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 线性回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RM</td>\n",
       "      <td>0.452377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ZN</td>\n",
       "      <td>0.129239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>RAD_24</td>\n",
       "      <td>0.103221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>B</td>\n",
       "      <td>0.078899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHAS</td>\n",
       "      <td>0.059660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>RAD_7</td>\n",
       "      <td>0.037664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>RAD_8</td>\n",
       "      <td>0.030071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>RAD_3</td>\n",
       "      <td>0.027465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>INDUS</td>\n",
       "      <td>0.013818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AGE</td>\n",
       "      <td>-0.001228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>RAD_4</td>\n",
       "      <td>-0.008698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>RAD_5</td>\n",
       "      <td>-0.011517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>RAD_2</td>\n",
       "      <td>-0.040970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>RAD_6</td>\n",
       "      <td>-0.055736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>RAD_1</td>\n",
       "      <td>-0.081501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>TAX</td>\n",
       "      <td>-0.113548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NOX</td>\n",
       "      <td>-0.151553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PTRATIO</td>\n",
       "      <td>-0.187898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CRIM</td>\n",
       "      <td>-0.221874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DIS</td>\n",
       "      <td>-0.386249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LSTAT</td>\n",
       "      <td>-0.476675</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    columns      coef\n",
       "5        RM  0.452377\n",
       "1        ZN  0.129239\n",
       "20   RAD_24  0.103221\n",
       "10        B  0.078899\n",
       "3      CHAS  0.059660\n",
       "18    RAD_7  0.037664\n",
       "19    RAD_8  0.030071\n",
       "14    RAD_3  0.027465\n",
       "2     INDUS  0.013818\n",
       "6       AGE -0.001228\n",
       "15    RAD_4 -0.008698\n",
       "16    RAD_5 -0.011517\n",
       "13    RAD_2 -0.040970\n",
       "17    RAD_6 -0.055736\n",
       "12    RAD_1 -0.081501\n",
       "8       TAX -0.113548\n",
       "4       NOX -0.151553\n",
       "9   PTRATIO -0.187898\n",
       "0      CRIM -0.221874\n",
       "7       DIS -0.386249\n",
       "11    LSTAT -0.476675"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 线性回归\n",
    "#class sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1)\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# 1.使用默认配置初始化学习器实例\n",
    "lr = LinearRegression()\n",
    "\n",
    "# 2.用训练数据训练模型参数\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# 3. 用训练好的模型对测试集进行预测\n",
    "y_test_pred_lr = lr.predict(X_test)\n",
    "y_train_pred_lr = lr.predict(X_train)\n",
    "\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(feat_names), \"coef\":list((lr.coef_.T))})\n",
    "fs.sort_values(by=['coef'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 岭回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is 0.6961684813069761\n",
      "The r2 score of RidgeCV on train is 0.7548524440445556\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "#岭回归／L2正则\n",
    "#class sklearn.linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, \n",
    "#                                  normalize=False, scoring=None, cv=None, gcv_mode=None, \n",
    "#                                  store_cv_values=False)\n",
    "from sklearn.linear_model import  RidgeCV\n",
    "\n",
    "#1. 设置超参数（正则参数）范围\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "#n_alphas = 20\n",
    "#alphas = np.logspace(-5,2,n_alphas)\n",
    "\n",
    "#2. 生成一个RidgeCV实例\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "\n",
    "#3. 模型训练\n",
    "ridge.fit(X_train, y_train)    \n",
    "\n",
    "#4. 预测\n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print ('The r2 score of RidgeCV on test is', r2_score(y_test, y_test_pred_ridge))\n",
    "print ('The r2 score of RidgeCV on train is', r2_score(y_train, y_train_pred_ridge))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lasso模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is 0.6945099371682093\n",
      "The r2 score of LassoCV on train is 0.7548861511225584\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "#### Lasso／L1正则\n",
    "# class sklearn.linear_model.LassoCV(eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, \n",
    "#                                    normalize=False, precompute=’auto’, max_iter=1000, \n",
    "#                                    tol=0.0001, copy_X=True, cv=None, verbose=False, n_jobs=1,\n",
    "#                                    positive=False, random_state=None, selection=’cyclic’)\n",
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "#1. 设置超参数搜索范围\n",
    "#alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "\n",
    "#2. 生成学习器实例\n",
    "#lasso = LassoCV(alphas=alphas)\n",
    "\n",
    "#1. 设置超参数搜索范围\n",
    "#Lasso可以自动确定最大的alpha，所以另一种设置alpha的方式是设置最小的alpha值（eps） 和 超参数的数目（n_alphas），\n",
    "#然后LassoCV对最小值和最大值之间在log域上均匀取值n_alphas个\n",
    "# np.logspace(np.log10(alpha_max * eps), np.log10(alpha_max),num=n_alphas)[::-1]\n",
    "\n",
    "#2 生成LassoCV实例（默认超参数搜索范围）\n",
    "lasso = LassoCV()  \n",
    "\n",
    "#3. 训练（内含CV）\n",
    "lasso.fit(X_train, y_train)  \n",
    "\n",
    "#4. 测试\n",
    "y_test_pred_lasso = lasso.predict(X_test)\n",
    "y_train_pred_lasso = lasso.predict(X_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print ('The r2 score of LassoCV on test is', r2_score(y_test, y_test_pred_lasso))\n",
    "print ('The r2 score of LassoCV on train is', r2_score(y_train, y_train_pred_lasso))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. 代码中给出了岭回归（RidgeCV）和Lasso（LassoCV）超参数（alpha_）调优的过程，请结合两个最佳模型以及最小二乘线性回归模型的结果，给出什么场合应该用岭回归，什么场合用Lasso，什么场合用最小二乘。（30分） \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一般来说，只要我们觉得数据有线性关系，LinearRegression类是我们的首先。如果发现拟合或者预测的不好，再考虑用其他的线性回归库。\n",
    "用LinearRegression类拟合的不是特别好，需要正则化，可以考虑用RidgeCV类,因为线性回归正则化有很多的变种，Ridge只是其中的一种。所以可能需要比选。如果输入特征的维度很高，而且是稀疏线性关系的话，RidgeCV类就不合适了。这时应该主要考虑Lasso回归类家族。\n",
    "LassoCV类是进行Lasso回归的首选。当我们面临在一堆高位特征中找出主要特征时，LassoCV类更是必选。当面对稀疏线性关系时，LassoCV也很好用。\n"
   ]
  }
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